A simple dataflow engine with scalable semantics.

The goal of pydra is to provide a lightweight Python dataflow engine for DAG construction, manipulation, and distributed execution.

Feature list:

1. Python 3.7+ using type annotation and attrs
2. Composable dataflows with simple node semantics. A dataflow can be a node of another dataflow.
3. splitter and combiner provides many ways of compressing complex loop semantics
4. Cached execution with support for a global cache across dataflows and users
5. Distributed execution, presently via ConcurrentFutures, SLURM, and Dask (this is an experimental implementation with limited testing)

[API Documentation] [PyCon 2020 Poster]

The Pydra Tutorial can be found in the pydra-tutorial repository.

The tutorial can be run locally (with the necessary requirements) or using Binder service:

Please note that mybinder times out after an hour.

pip install pydra

Pydra requires Python 3.7+. To install in developer mode:

git clone [email protected]:nipype/pydra.git
cd pydra
pip install -e .[dev]

If you want to test execution with Dask:

git clone [email protected]:nipype/pydra.git
cd pydra
pip install -e .[dask]

It is also useful to install pre-commit:

pip install pre-commit
pre-commit